Systems biology and the availability of genomic data have revolutionized our ability to identify the underlying biological mechanisms of diverse diseases and disorders of behavior. Indeed it is becoming within our reach to reclassify diseases based on biological substrate, rather than external manifestation. Using combinatorial, graph-centered approaches, we are able to infer relations among behavioral disorders, addiction-related phenotypes and causative pathways based on gene-phenotype associations. We integrate knowledge of biological functions based on associated molecular networks using The Ontological Discovery Environment, our web based software system designed to enable biologists to perform integrated analysis of phenotype centered genomic data. We make use of advances in database design, algorithms, and advanced mouse resources. In this proposal, we address all three of these areas, and propose to demonstrate our tools for the study of relations among stress, anxiety, psychological disorders and the sensitivity toward and use of drugs and alcohol. Our findings will be validated through the use of mouse models. The three aims of the project are to develop a large data repository for behavioral neuroscience and data structures that more efficiently enable the use of databases to represent graphical network data, to develop algorithms for the analysis of integrated gene centered data across species and experimental platforms to incorporate these developments into a Web-based software system, and to use this tool to find genes underlying relationships between multiple abused substances and behavior. PUBLIC HEALTH RELEVANCE: A wealth of data is being generated that associates multiple genes to multiple behavioral disorders and addiction-related phenotypes. The Ontological Discovery Environment project enables the integration of this data across species and experimental systems based on gene-phenotype associations. The methods and software we develop will enable enhanced understanding of the relationships among behavioral traits and disorders of addiction and of the underlying molecular mechanisms underlying classes of diseases. We will identify and test the role of specific genes in drug addiction. Our methods will also facilitate the mapping of research in non-human model organisms onto human disease. A major component of this effort is software development and training to ensure that the technical advances are translated into deliverable tools for the research community to perform similar analyses.